In , the restricted Neyman-Pearson (RNP) approach is investigated. RNP is similar to the restricted Bayesian approach in the sense that, this time, the prior distribution of the parameter in the composite hypothesistestingframework is assumed to be known with some uncertainty. In the composite Neyman-Pearson hypothesistestingframework, both of the null and alternative hypotheses can be composite. For the null hypothesis, the general approach is to apply a false alarm constraint for all values of the parameter. For the alternative hypothesis, however, there are a number of methods to employ. Two of these methods are as follows: In the ﬁrst one, the average detection probability is maximized, which is called the max-mean approach. In the second one, the minimum of the detection probabilities is maximized, which is called the max-min approach. Note that the former assumes perfect knowledge of the prior distribution of the parameter whereas the latter assumes no such knowledge. In this respect, the former and latter are similar to the Bayesian and minimax approaches respectively. What the RNP approach does is to compromise the max-mean and max-min approaches. In other words, in the RNP framework, the aim is to maximize the average detection probability under constraints on the worst-case detection and false- alarm probabilities [2, 3]. It is worthwhile to note that since this approach uses the available prior distribution information to a degree, it encompasses both probabilistic and nonprobabilistic descriptions of uncertainty.
Hypothesistesting based framework for key recovery attacks: We show that the data complexity results from [32, 19, 11] can be recovered by following the hypothesistestingframework for analysing key recovery attacks. This framework appears in the literature in the context of analysing distinguishing attacks and does not seem to have been seriously considered for analysing key recovery attacks. We find this to be surprising, since the hypothesistestingframework is simpler and avoids the restrictions of the order statistics based approach. In re-deriving the results of [19, 11], we make use of normal approximations of a χ 2 -based and an LLR-based test statistics used in these works which did not, however, consider issues related to the error in such approximations. On the appropriateness of using the χ 2 and the LLR-based test statistics: The χ 2 based test statistics used in [19, 11] requires several approximations. Carefully analysing these show some very surprising results. We mention these as applied to multiple linear cryptanalysis. These also hold for multiple differential cryptanalysis, though to a lesser extent.
The binomial and interval forecast evaluation methods are based on a hypothesis-testingframework and are used to test the null hypothesis that the reported VaR estimates are “acceptably accurate,” where accuracy is defined by the test conducted. As shown in the simulation exercise, the power of these tests can be low against reason- able alternative VaR models. This result does not negate their usefulness, but it does indicate that the inference drawn from this analysis has limitations.
In the present work, the classification task is cast in a hypothesistestingframework as well. However, the objec- tive – thus, the novelty – is to define not only a classifier, but the means for evaluating the multimodal classifica- tion chain – or pattern recognition process – performance. To this end, the hypothesis tests are defined using the Neyman-Pearson frequentist approach  and one test is associated to each potential mouth region. This way, the ability of the classifier to produce good relative instance scores can be measured. Moreover, an evaluation of the whole pattern recognition process, including the feature extraction step, can be introduced. It allows to assess the benefit of optimizing features prior to performing the classification.
DOI: 10.4236/oalib.1105584 2 Open Access Library Journal testing for a cross-covariance matrix being equal to a specified one becomes an important issue. For instance, when testing for time-reversibility (see Section 3 for more details), we can transform the problem into the one of cross-covariance matrix test. Therefore, like the covariance matrix test, it is also of great practical interest to develop methods for the cross-covariance matrix test.
When a statistical test of hypothesis for a population mean is performed, we are faced with the possibility of committing a Type II error by not rejecting the null hypothesis when in fact the population mean has changed. We consider this issue and quantify matters in a manner that differs a bit from what is commonly done. In particular, we define the probability distribution function for Type II errors. We then explore some interesting properties that we have not seen mentioned elsewhere for this probability distribution function. Finally, we discuss several Maple procedures that can be used to perform various calculations using the dis- tribution.
Herein we took advantage of our domain experts to build a simplified and a tractable version of a causal- ity hypothesis graph of cartilage degradation during to osteoarthritis, and to validate our methodology for confi- dence assessment of causality hypothesis. The evaluation results, the feedback from our experts, and the lessons learnt from this overall experience allow us to conclude that a methodology for shared hypothesistesting could be incorporated as an invaluable asset to the online bio- logical knowledge graph mining services. In particular, our hypothesis graph construction methodology could be used routinely to enrich biological knowledge graphs (e.g., Knowledge Bio ) and online databases (e.g., Gene Wiki ) by extracting the causality relation- ships information from OWL 2 ontologies. Of course, the proposed set of patterns for the normalization of the hypothesis graph will have to be augmented and tuned for a specific studied context. We, for instance, defined graph rewriting normalization patterns to deal with complementary biological scenarios of simultane- ously positive and negative regulations of biological pro- cesses (see “Robustness of the system in presence of complementary causality relationships” section). In fact, the graph rewriting patterns is a general paradigm for the transformation of formalized knowledge on a spe- cific biological pattern into its equivalent graph rep- resentation and might open an opportunity for more research and practical contributions from the biomedical community.
The REH have invited some fierce theoretical criticisms suggesting Ricardian Non Equivalence. Firstly, according to the Diamond Overlapping Generations Model, the REH do not factor in population dynamics and assumes infinite horizons. If the economy is of the Samuelson (1958) and Diamond (1965) type in which individuals live in exactly successive periods of overlapping generations and derive utility from their own consumption, the REH will not hold (Seater, 1993). Issues of government debt in the current period lowers taxes of the current working generation to be paid with taxes levied on future generations since the present value of future tax obligations of the current generations will be less than the current value of tax reduction. In the end, the bonds represent net wealth to those who are currently living, thereby inducing their consumption (Romer, 1996) and thus failing the hypothesis.
In evaluating structural models or inner models with PLS by looking at the magnitude of the percentage variance explained by the R-Squares value for each endogenous latent variable as the predictive power of structural capital, Stone-Geisser (Geisser 1975; Stone 1974 in Ghozali, 2014; 94) it is test to testing the predictive relevance and Goodness of fit (GoF) to measure overall model fit. To predict causality in SEM-PLS using the warpPLS 3.0 program which can be measured by:
In the hypothesis-generating study the researcher’ s re- sponsibility to annotate participants’ genomes/exomes is ongoing. This is ongoing because, as noted above, one of the experimental aims is to study the motivations and interests of the subjects in these types of results. Deter- mining how this motivation and interest fares over time is an important research goal. During the informed con- sent discussion it is emphasized that the iterative nature of result interpretation will lead to multiple meetings for the disclosure of clinically actionable results, and that the participant may be contacted months or years after the date of enrollment. Additionally, it is outlined that the participant will make a choice about learning the re- sult each time he/she is re-contacted about the availabil- ity of a research finding, and that finding will only be confirmed in a CLIA-certified laboratory if the partici- pant opts to learn the information. Participants who re- turn to discuss results are reminded that they will be contacted in the future if and when other results deemed to be clinically actionable are found for that individual.
The first hypothesis (H1), there is a positive and significant influence on entrepreneurship education on the motivation of entrepreneurship students in vocational schools in East Java. Based on table 1, it is known that entrepreneurship education shows the value of t-count 16.436> t-table 1.9691, and the significance of t is 0.000 <0.05, so it can state that H0 is rejected and Ha is accepted. So it can be concluded that in this study, entrepreneurship education has a positive and significant effect on entrepreneurial motivation. Testing shows that these results support the first hypothesis (H1) in this study. Second, hypothesis (H2), there is a positive and significant influence on entrepreneurship education on the interest in entrepreneurship of vocational students in East Java. Based on table 2, show that entrepreneurship education shows the value of t-count 5.352> t table 1.9691, and the significance of t is 0.000 <0.05, so it can state that H0 is rejected and Ha is accepted. So it can be concluded that in this study, entrepreneurship education has a positive and significant effect on entrepreneurial interest. Testing shows that these results support the first hypothesis (H2) in this study. Third hypothesis (H3), there is a positive and significant influence on entrepreneurial motivation influencing the entrepreneurial interest of vocational students in East Java. Based on table 2, it can be seen that entrepreneurship education shows the value of t-count 8.391> t table 1.9691, and the significance of t is 0.000 <0.05, so it can be stated that H0 is rejected and Ha is accepted. So it can be concluded that in this study, the motivation of entrepreneurship has a positive and significant effect on entrepreneurial interest. Testing shows that these results support the first hypothesis (H3) in this study. Fourth hypothesis (H4), there is a positive and significant influence on entrepreneurship education affecting the interest of entrepreneurship through the motivation of SMK
Since calculated values of Trace exceed the tabled critical value of 35.06 at the 0.05 level, the test statistics for each country result in rejection of no cointegration among variables and accept one of the alternative hypothesis that there are one or more cointegrating vectors. Furthermore, the same test statistics indicate that the alternative hypothesis that there are two or three cointegrating vectors should be accepted at the 0.05 level for Chile, Finland, Italy, Malaysia, Singapore, South Africa, Sweden, Turkey and the USA. Finally the Trace test statistic indicates that H 1 : r =3 is accepted at the 0.05 level
This work is motivated by a dataset of diffusion tensor imaging (DTI) of intracranial white matter for patients with multiple sclerosis (MS), a neurodegenerative disease characterized by damage to the myelin sheath that causes degradation of physical and mental ability (see Reich et al. (2010) for study details). DTI measures the diffusion of water in the brain and can be used to map demyelination of white matter. These DTI scans are summarized as profiles that measure a magnetic resonance imaging (MRI) index, such as mean diffusivity or fractional anisotropy, as a function of location along white matter tracts. Many studies use functional models to map the relationship between tract profiles and MS status or disability progression (Gertheiss et al., 2013; Goldsmith et al., 2011, 2012; Zhu et al., 2010; Ivanescu et al., 2015). We focus on the study in Goldsmith et al. (2011) who attempt to identify patients with MS using logistic regression models that include (a) no tract profile information, (b) tract profile average only, and (c) full tract profile as a functional predictor. Hypothesistesting can provide a scientifically rigorous approach to determine which tract profiles are related to MS. Additionally, testing can determine if modeling the full tract profile is significantly better than simply modeling its average, that is, comparing standard versus functional logistic regression models. To the authors’ knowledge, there are no existing methods for binary (generalized) responses.
to address this testing problem, and provide an easy-to-use software implemen- tation. Our approach is applicable to a variety of realistic scenarios, such as 1) curves observed at dense or sparse grids of points, with or without measurement error, 2) different sampling designs across the samples, and 3) different sample sizes. The methodology scales well with the total sample size, and it can be
Hence, the resulting rectangular set will contain only points in which the null hypothesis was rejected. Points outside the rectangular set are excluded from the rectangular set because they may not be added while maintaining the rectangular requirement. Using this strategy, largest rectangular set, the Bonferroni algorithm will report a smaller aver- age area than it reported when no particular resulting shape was required. In addition, comparing novel methods to the Bonferroni method, here, is a comparison of methods that all produce rectangular sets.
The auditory forebrain regions NCM and CMM of songbirds are associated with perception and complex auditory processing. Expression of the immediate-early gene ZENK varies in response to different sounds. Two hypotheses are proposed for this. First, ZENK may reflect access to a representation of song memories. Second, ZENK may reflect attention. I tested these hypotheses by measuring ZENK in response to tutored heterospecific or isolate songs compared to non-tutored wild-type song. Young zebra finch females were exposed to different tutoring conditions and later exposed to different playbacks, and the expression of ZENK in CMM and NCM measured. ZENK responses varied across playback stimuli in some brain regions, but did not interact with tutoring conditions. These results do not support the hypothesis that ZENK activation reflects auditory memories.
In this article we review and demonstrate the hypothesis tests for both a single propor- tion and a comparison of two independent proportions. The topics covered may provide a basic understanding of the quantitative approaches for analyzing radiologic data. De- tailed information on these concepts may be found in both introductory (5,6) and advanced textbooks (7–9). Related links on the World Wide Web are listed in Appendix A.
A framework has a reverse just on the off chance that it is square and still, after all that lone on the off chance that it is nonsingular. Typically particular and rectangular grids don't have opposite. As of late needs have been felt in various zones of connected Mathematics for some sort of halfway backwards of a network that is solitary or even rectangular. Such converse are called summed up reverse. The idea of summed up converse was presented first by Moore in 1920 and autonomously rediscovered by Penrose in 1955. Penrose demonstrated that, for each limited grid A (square or rectangular) of Real (or complex) components,
In this work, we start by giving some basic notions on asymmetric QHT and briefly reviewing the QHB, also showing how its computation simply reduces to the quantum fidelity  in the presence of pure states. Then, we provide a general recipe for computing this bound in the case of multimode Gaussian states, for which it can be expressed in terms of their first- and second-order statistical moments. In the general multimode case, we derive a relation between the QHB and other easier-to-compute bounds, which are based on well-known mathematical inequalities. Finally, we derive analytical formulas and numerical results for the most important classes of one-mode and two-mode Gaussian states. By developing the theory of asymmetric QHT for Gaussian states, our work could be useful in tasks and protocols involving Gaussian quantum information , including tech- nological applications of quantum channel discrimination (e.g., quantum illumination [13,14] or quantum reading [15–18]) where we are interested in increasing our ability to accept one specific quantum hypothesis.